Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

The traditional vehicular roll-jam attack is an effective means to gain access to the target vehicle by jamming and recording key fob inputs from a victim. However, it requires specific knowledge of the attack surface, and delicate tuning of software-defined radio parameters. We have developed an enhanced version of the roll-jam attack that uses a known noise signal for jamming, in contrast to the additive white Gaussian noise that is typically used in the attack. Using a known noise signal allows for less strict tuning of the software-defined radios used in the attack, and allows for digital noise removal of the recorded input to enhance the replay attack.

View More Papers

LOKI: State-Aware Fuzzing Framework for the Implementation of Blockchain...

Fuchen Ma (Tsinghua University), Yuanliang Chen (Tsinghua University), Meng Ren (Tsinghua University), Yuanhang Zhou (Tsinghua University), Yu Jiang (Tsinghua University), Ting Chen (University of Electronic Science and Technology of China), Huizhong Li (WeBank), Jiaguang Sun (School of Software, Tsinghua University)

Read More

Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning

Sampath Rajapaksha (Robert Gordon University), Harsha Kalutarage (Robert Gordon University), M.Omar Al-Kadri (Birmingham City University), Andrei Petrovski (Robert Gordon University), Garikayi Madzudzo (Horiba Mira Ltd)

Read More

Evasion Attacks and Defenses on Smart Home Physical Event...

Muslum Ozgur Ozmen (Purdue University), Ruoyu Song (Purdue University), Habiba Farrukh (Purdue University), Z. Berkay Celik (Purdue University)

Read More

PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou (Nanjing University of Science and Technology), Yansong Gao (Nanjing University of Science and Technology), Anmin Fu (Nanjing University of Science and Technology), Kai Chen (Chinese Academy of Science), Zhiyang Dai (Nanjing University of Science and Technology), Zhi Zhang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Yuqing Zhang (University of Chinese Academy of Science)

Read More